附加力外环的机器人力／位置自适应模糊控制

提出一种由神经网络训练模糊控制规则的自适应模糊控制器,并应用附加力外环的机器人力／位置控制。在不改变一般工业机器人原有位置控制的前提下,实现力／位置自适应模糊控制。实验结果表明,该方法可使机器人控制系统对工作环境接触刚度的自适应能力得到显著改善。     

Position-Based Fuzzy Force Control for Dual Industrial Robots

In this paper, a fuzzy force control framework is proposed for dual-industrial robot systems. The master/slave control method is used in dual-robot systems. Two MITSUBISHI MELFA RV-M1 industrial robots, one is equipped with an BL Force/Torque sensor and the other is not, are utilized for implementing the dual-arm system. In order to adapt various stiffness of the holding object, an adaptable fuzzy force control scheme has been proposed to improve the performance. The ability of the adaptable force control system is achieved by tuning the scaling factor of the fuzzy logic controller. Successful experiments are carried out for the dual-robot system handling an object.     

A Study on Kinematics and Workspace Determination of a General 6-<Emphasis Type="Underline">P</Emphasis> US Robot

This paper proposes the novel adaptive neural network (ADNN) compliant force/position control algorithm applied to a highly nonlinear serial pneumatic artificial muscle (PAM) robot as to improve its compliant force/position output performance. Based on the new adaptive neural ADNN model which is dynamically identified to adapt well all nonlinear features of the 2-axes serial PAM robot, a new hybrid adaptive neural ADNN-PID controller was initiatively implemented for compliant force/position controlling the serial PAM robot system used as an elbow and wrist rehabilitation robot which is subjected to not only the internal coupled-effects interactions but also the external end-effecter contact force variations (from 10[N] up to critical value 30[N]). The experiment results have proved the feasibility of the new control approach compared with the optimal PID control approach. The novel proposed hybrid adaptive neural ADNN-PID compliant force/position controller successfully guides the upper limb of subject to follow the linear and circular trajectories under different variable end-effecter contact force levels.     

A Proposed Hybrid Recurrent Neural Control System for Two Co-operating Robots

This paper discusses a model refernce adaptive (MRAC) position/force controller using proposed neural networks for two co-operating planar robots. The proposed neural network is a recurrent hybrid network. The recurrent networks have feedback connections and thus an inherent memory for dynamics, which makes them suitable for representing dynamic systems. A feature of the networks adopted is their hybrid hidden layer, which includes both linear and nonlinear neurons. On the other hand, the results of the case of a single robot under position control alone are presented for comparison. The results presented show the superior ability of the proposed neural network based model reference adaptive control scheme at adapting to changes in the dynamics parameters of robots.     

Fuzzy-neuro position/force control for robotic manipulators with uncertainties

The performance of a controller for robot force tracking is affected by the uncertainties in both the robot dynamic model and the environmental stiffness. This paper aims to improve the controller’s robustness by applying the neural network to compensate for the uncertainties of the robot model at the input trajectory level rather than at the joint torque level. A self-adaptive fuzzy controller is introduced for robotic manipulator position/force control. Simulation results based on a two-degrees of freedom robot show that highly robust position/force tracking can be achieved, despite the existence of large uncertainties in the robot model.     

Diagnostic neural adaptive control of drifting systems

A hierarchical network of neural network planning and control is employed to successfully accomplish a task such as grasping in a cluttered real world environment. In order for the individual robot joint controllers to follow their specific reference commands, information is shared with other neural network controllers and planners within the hierarchy. Each joint controller is initialized with weights that will acceptably control given a change in any of several crucial parameters across a broad operating range. When increased accuracy is needed as parameters drift, the diagnostic node fuzzy supervisor interprets the controller network's diagnostic outputs and transitions the weights to a closest fit specificchild controller. Future reference commands are in turn influenced by the diagnostic outputs of every robot joint neural network controller. The neural network controller and diagnostics are demonstrated for linear and nonlinear plants.     

Guidance algorithm for complex-shape peg-in-hole strategy based on geometrical information and force control

This paper suggests a solution for peg-in-hole problems involving complex geometry. Successful completion of peg-in-hole assembly tasks depends on a geometry-based approach for determining the guiding direction, fine contact motion control, and a reference force for the alignment/insertion process as well. Therefore, in this study, we propose a peg-in-hole strategy for complex-shaped parts based on a guidance algorithm. This guidance algorithm is inspired by the study of human motion patterns; that is, the assembly direction selection process and the maximum force threshold are determined through the observation of humans performing similar actions. In order to carry out assembly tasks, an assembly direction is chosen using the spatial arrangement and geometric information of complex-shaped parts, and the required force is decided by kinesthetic teaching with a Gaussian mixture model. In addition, an impedance controller using an admittance filter is implemented to achieve stable contact motion for a position control-based industrial robot. The performance of the proposed assembly strategy was evaluated by experiments using arbitrarily complex-shaped parts with different initial situations.     

Self-calibrated robotic manipulator force observer

In the robotic manipulation context, end-effector contact forces may be difficult to measure mainly due to the tool dynamic interferences such as the inertial forces. In this paper, a whole methodology is proposed to estimate these forces. The new approach is based on a sensor fusion technique that integrates the information of a wrist force sensor, of a 3D accelerometer placed at the robot tool and the joint position sensors measurements. The proposed methodology not only offers a suitable estimator in terms of response and filtering, but also presents a self-calibrating feature that allows an easy integration into any industrial setup. To experimentally validate the performance of the proposed methodology, two different industrial manipulators were used: an ABB robot and a Stäubli robot, both with open control system architectures. An impedance control scheme was used as force/position control law to demonstrate the need and results of the proposed calibration result.     

A neural network-based approach for an assembly cell control

In several robotics applications, the robot must interact with the workspace, and thus its motion is constrained by the task. In this case, pure position control will be ineffective since forces appearing during the contacts must also be controlled. However, simultaneous position and force control called hybrid control is then required. Moreover, the nonlinear plant dynamics, the complexity of the dynamic parameters determination and computation constraints makes more difficult the synthesis of control laws. In order to satisfy all these constraints, an effective hybrid force/position approach based on artificial neural networks for multi-inputs/multi-outputs systems is proposed. This approach realizes, simultaneously, an identification and control of systems, and it is implemented according to two phases: At first, a neural observer is trained off-line on the basis of the data acquired during contact motion, in order to realize a smooth transition from free to contact motion. Then, an online learning of the neural controller is implemented using neural observer parameters so that the closed-loop system maintains a good performance and compensates for uncertain/unknown dynamics of the robot and the environment. A typical example on which we shall focus is an assembly task. Experimental results on a C5 links parallel robot demonstrate that the robot's skill improves effectively and the force control performances are satisfactory, even if the dynamics of the robot and the environment change.     

非完整移动机器人的神经网络鲁棒自适应控制

在非完整移动机器人轨迹跟踪问题中,针对机器人运动学与动力学模型的参数和非参数不确定性,提出了一种混合神经网络鲁棒自适应轨迹跟踪控制器,该控制器由运动学控制器和动力学控制器两部分组成;其中,采用了参数自适应的径向基神经网络对运动学模型的未知部分进行了建模,并采用权值在线调整的单层神经网络和自适应鲁棒控制项构成了动力学控制器;基于Lyapunov方法的设计过程保证了系统的稳定性和收敛性,仿真结果证明了算法的有效性。     

Motion/force control scheme for electrically driven cooperative multiple mobile manipulators

This paper presents the motion and force control problem of rigid-link electrically driven cooperative mobile manipulators handling a rigid object. Although, the motion/force control problem of cooperative mobile manipulators has been enthusiastically studied. But there is little research on the motion/force control of electrically driven cooperative mobile manipulators. Due to the inclusion of the actuator dynamics with the manipulator’s dynamics, the controller exhibits some important characteristics. For the electromechanical system, we have designed a novel controller at the dynamic level as well as at the actuator level. In the proposed control scheme, at the dynamic level, uncertain non-linear mechanical dynamics is approximated with a hybrid controller containing model-based control scheme combined with model-free neural network based control scheme together with an adaptive bound. The adaptive bound is used to suppress the effects of external disturbances, friction terms, and reconstruction error of the neural network. At the actuator level, for the approximation of the unknown electrical dynamics, the model-free neural network is utilized. The developed control scheme provides that the position tracking errors, as well as the internal force, converge to the desired levels. Additionally, direct current motors are also controlled in such a way that the desired currents and torques can be attained. In order to make the overall system to be asymptotically stable, online learning of the weights and the parameter adaptation of the parameters is utilized in the Lyapunov function. The superiority of the developed control method is carried out with the numerical simulation results and its superior robustness is shown in a comparative manner.     

悬挂伸缩刀具的树障清理空中机器人飞行控制方法

针对输电线路附近的树障进行清理问题,本文提出了一种新型的悬挂伸缩刀具的树障清理空中机器人并进行了仿真和实物验证.首先,对悬挂伸缩刀具的空中机器人进行了伸缩刀具重心变化下的动力学、运动学建模及接触建模.其次,为避免空中机器人接触作业时机器人倾翻的问题,设计了力估计器用于力感知和导纳控制器用于力控制.针对空中机器人非线性强耦合、伸缩刀具时参数摄动及作业时扰动的问题,设计了线性自抗扰控制(LADRC)的机器人位姿控制器.再次,数值仿真验证了导纳控制能有效避免空中机器人接触作业时产生倾翻的问题,以及基于LADRC控制器的位姿控制具有良好的稳定性和抗扰性.最后,通过实物飞行和接触作业测试,进一步验证了本文悬挂伸缩刀具的树障清理空中机器人及其控制方法的有效性.     

Adaptive recurrent neural network control of uncertain constrained nonholonomic mobile manipulators

In this article, motion/force control problem of a class of constrained mobile manipulators with unknown dynamics is considered. The system is subject to both holonomic and nonholonomic constraints. An adaptive recurrent neural network controller is proposed to deal with the unmodelled system dynamics. The proposed control strategy guarantees that the system motion asymptotically converges to the desired manifold while the constraint force remains bounded. In addition, an adaptive method is proposed to identify the contact surface. Simulation studies are carried out to verify the validation of the proposed approach.     

Adaptive neural controller for cooperative multiple robot manipulator system manipulating a single rigid object

In this article, an adaptive neural controller is developed for cooperative multiple robot manipulator system carrying and manipulating a common rigid object. In coordinated manipulation of a single object using multiple robot manipulators simultaneous control of the object motion and the internal force exerted by manipulators on the object is required. Firstly, an integrated dynamic model of the manipulators and the object is derived in terms of object position and orientation as the states of the derived model. Based on this model, a controller is proposed that achieves required trajectory tracking of the object as well as tracking of the desired internal forces arising in the system. A feedforward neural network is employed to learn the unknown dynamics of robot manipulators and the object. It is shown that the neural network can cope with the unknown nonlinearities through the adaptive learning process and requires no preliminary offline learning. The adaptive learning algorithm is derived from Lyapunov stability analysis so that both error convergence and tracking stability are guaranteed in the closed loop system. Finally, simulation studies and analysis are carried out for two three-link planar manipulators moving a circular disc on specified trajectory.     

Neural network-based Smith predictor design for the time-delay in a tele-operated control system

This article presents a methodology for compensating for the time-delay effects in tele-operated control systems. Compensation can be carried out by a neural network. A tele-operated system consists of a master robot to give commands, and a slave robot to work with the environment. The positional command by the master robot is transferred to the slave robot, and the contact force from the environment is transferred back to the master robot. The structure of the Smith predictor is modified by replacing the linear estimator with a neural network whose structure is based on the radial basis function (RBF). The RBF network identifies the slave model to deal with the nonlinearities in the system. Simulation studies have been conducted, and experimental studies of one-directional force control were performed to confirm the simulation results.     

Teleoperation Control of a Position‐Based Impedance Force Controlled Mobile Robot by Neural Network Learning: Experimental Studies

Effective haptic performance in teleoperation control systems can be achieved by solving two major problems: the time‐delay in communication channels and the transparency of force control. The time‐delay in communication channels causes poor performance and even instability in a system. The transparency of force feedback is important for an operator to improve the performance of a given task. This article suggests a possible solution for these two problems through the implementation of a teleoperation control system between the master haptic device and the slave mobile robot. Regulation of the contact force in the slave mobile robot is achieved by introducing a position‐based impedance force control scheme in the slave robot. The time‐delay problem is addressed by forming a Smith predictor configuration in the teleoperation control environment. The configuration of the Smith predictor structure takes the time‐delay term out of the characteristic equation in order to make the system stable when the system model is given a priori. Since the Smith predictor is formulated from exact linear modeling, a neural network is employed to identify and model the slave robot system as a nonlinear model estimator. Simulation studies of several control schemes are performed. Experimental studies are conducted to verify the performance of the proposed control scheme by regulating the contact force of a mobile robot through the master haptic device.     

Robust dynamic surface control of flexible joint robots using recurrent neural networks

A robust neuro-adaptive controller for uncertain flexible joint robots is presented. This control scheme integrates H-infinity disturbance attenuation design and recurrent neural network adaptive control technique into the dynamic surface control framework. Two recurrent neural networks are used to adaptively learn the uncertain functions in a flexible joint robot. Then, the effects of approximation error and filter error on the tracking performance are attenuated to a prescribed level by the embedded H-infinity controller, so that the desired H-infinity tracking performance can be achieved. Finally, simulation results verify the effectiveness of the proposed control scheme.     

External force control of an industrial puma 560 robot

Deformations occurring in a robot working in contact with a rigid environment is a real problem in the industrial world. This problem can be solved by taking into account forces undergone by the robot at the end-effector level. The first part of this article is aimed at determining a force control scheme that satisfies this constraint and that can be implemented on a non-modified industrial robot controller. Various existing force control schemes are investigated and the reasons for discarding them are given. Then, the emphasis is put on a so-called external force control scheme, which seems to be a solution to our problem. The control law appearing in such a scheme is determined by means of a realistic robot simulator developed on a SUN workstation. A simple integral term on the force error gives acceptable results in various robot configurations. This is illustrated in the form of graphs. In a second part, the implementation of this external control scheme in a real PUMA 560 robot's UNIMATE controller is presented. Certain software issues and implementation solutions are pointed out. Several experiments are described. Once again, graphs are used to show the experimental results. © 1994 John Wiley & Sons, Inc.     

Decoupling force and motion control in industrial robots

A theoretical approach to force control design for industrial robots involved in surface-following tasks is described in this paper, assuming an infinitely stiff environment. Independent Joint Control techniques, based on standard (PID) algorithms, are adopted for position control. Force control acts as an outer loop, by adding a bias to the position set points in the joint space. A simple model and compensation of the joint flexibility effects, that play an important role in determining the dynamic behavior of the system, are also presented, leading to a force control decoupled from motion control. Some experimental results are discussed, with reference to the industrial robot SMART.     

Impedance Control with On-Line Neural Network Compensator for Dual-Arm Robots

A controller design strategy of dual-arm robots is proposed in this paper. The controller consists of a central controller and three force controllers. The central controller is used to calculate each arms force command according to the desired object motion. A force controller is used in each arm to track the commanding force. Another force controller is used to track the desired contact force between the manipulated object and its environment. The force controller can be partitioned into three parts. The computed torque method is used to linearize and decouple the dynamics of a manipulator. An impedance controller is then added to regulate the mechanical impedance between the manipulator and its environment. In order to track a reference force signal, an on-line neural network is used to compensate the effect of unknown parameters of the manipulator and environment. The simulation results are reported to show the performance of the neural network compensator.